{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understanding Advantage Actor-Critic (A2C): A Complete Guide\n",
    "\n",
    "# Table of Contents\n",
    "\n",
    "- [Introduction](#introduction)\n",
    "- [What is Actor-Critic?](#what-is-actor-critic)\n",
    "- [What is A2C?](#what-is-a2c)\n",
    "  - [Synchronous vs. Asynchronous (A3C)](#synchronous-vs-asynchronous-a3c)\n",
    "  - [Why Use Advantages?](#why-use-advantages)\n",
    "- [Where and How A2C is Used](#where-and-how-a2c-is-used)\n",
    "- [Mathematical Foundation of A2C](#mathematical-foundation-of-a2c)\n",
    "  - [Actor Update (Policy Gradient with Advantage)](#actor-update-policy-gradient-with-advantage)\n",
    "  - [Critic Update (Value Function Estimation)](#critic-update-value-function-estimation)\n",
    "  - [Advantage Estimation (n-step or GAE)](#advantage-estimation-n-step-or-gae)\n",
    "  - [Entropy Bonus](#entropy-bonus)\n",
    "  - [Combined Loss](#combined-loss)\n",
    "- [Step-by-Step Explanation of A2C](#step-by-step-explanation-of-a2c)\n",
    "- [Key Components of A2C](#key-components-of-a2c)\n",
    "  - [Policy Network (Actor)](#policy-network-actor)\n",
    "  - [Value Network (Critic)](#value-network-critic)\n",
    "  - [Rollout Collection (On-Policy, Batched)](#rollout-collection-on-policy-batched)\n",
    "  - [Advantage Estimation](#advantage-estimation)\n",
    "  - [Combined Actor-Critic Loss](#combined-actor-critic-loss)\n",
    "  - [Synchronous Updates](#synchronous-updates)\n",
    "  - [Hyperparameters](#hyperparameters)\n",
    "- [Practical Example: Custom Grid World](#practical-example-custom-grid-world)\n",
    "- [Setting up the Environment](#setting-up-the-environment)\n",
    "- [Creating the Custom Environment](#creating-the-custom-environment)\n",
    "- [Implementing the A2C Algorithm](#implementing-the-a2c-algorithm)\n",
    "  - [Defining the Actor Network](#defining-the-actor-network)\n",
    "  - [Defining the Critic Network](#defining-the-critic-network)\n",
    "  - [Calculating Returns and Advantages (using GAE)](#calculating-returns-and-advantages-using-gae)\n",
    "  - [The A2C Update Step](#the-a2c-update-step)\n",
    "- [Running the A2C Algorithm](#running-the-a2c-algorithm)\n",
    "  - [Hyperparameter Setup](#hyperparameter-setup)\n",
    "  - [Initialization](#initialization)\n",
    "  - [Training Loop](#training-loop)\n",
    "- [Visualizing the Learning Process](#visualizing-the-learning-process)\n",
    "- [Analyzing the Learned Policy (Optional Visualization)](#analyzing-the-learned-policy-optional-visualization)\n",
    "- [Common Challenges and Solutions in A2C](#common-challenges-and-solutions-in-a2c)\n",
    "- [Conclusion](#conclusion)\n",
    "\n",
    "## Introduction\n",
    "\n",
    "Actor-Critic methods are a family of reinforcement learning algorithms that combine the strengths of both policy-based methods (like REINFORCE) and value-based methods (like Q-learning). They maintain two distinct components: an Actor, which learns and executes the policy, and a Critic, which learns a value function to evaluate the states or state-action pairs visited by the actor. Advantage Actor-Critic (A2C) is a popular and effective synchronous variant of these methods.\n",
    "\n",
    "## What is Actor-Critic?\n",
    "\n",
    "The core idea is to use the Critic's value estimates to improve the Actor's policy updates. Instead of using the high-variance Monte Carlo return $G_t$ (as in REINFORCE) to weight the policy gradient $\\nabla_\\theta \\log \\pi_\\theta(a_t|s_t)$, actor-critic methods use a lower-variance signal derived from the Critic.\n",
    "\n",
    "- **Actor:** A policy network $\\pi(a|s; \\theta)$ that outputs action probabilities (or determines actions directly).\n",
    "- **Critic:** A value network (e.g., $V(s; \\phi)$ or $Q(s, a; \\phi)$) that estimates how good a state or state-action pair is.\n",
    "\n",
    "The Critic learns from the temporal difference (TD) errors based on the rewards received, and the Actor updates its policy in the direction suggested by the Critic's evaluations (often using the TD error or the Advantage function as the scaling factor for the policy gradient).\n",
    "\n",
    "## What is A2C?\n",
    "\n",
    "Advantage Actor-Critic (A2C) is a specific implementation strategy for actor-critic methods. Its key features are:\n",
    "\n",
    "1.  **Advantage Function:** It explicitly uses the *advantage function* $A(s_t, a_t) = Q(s_t, a_t) - V(s_t)$ to update the policy. Intuitively, $A(s_t, a_t)$ measures *how much better* taking action $a_t$ in state $s_t$ is compared to the average action from that state according to the current policy.\n",
    "2.  **State-Value Critic:** The critic typically learns the state-value function $V(s; \\phi)$. The advantage is then estimated using this critic, often via n-step returns or GAE: $\\hat{A}_t \\approx R_t - V(s_t; \\phi)$, where $R_t$ is an estimate of the total return from state $s_t$.\n",
    "3.  **Synchronous Updates:** Unlike its predecessor A3C (Asynchronous Advantage Actor-Critic), A2C performs updates synchronously. A central controller waits for a batch of experience to be collected (often from multiple parallel workers, though implemented sequentially here) before computing gradients and updating the shared actor and critic networks.\n",
    "\n",
    "### Synchronous vs. Asynchronous (A3C)\n",
    "- **A3C:** Uses multiple parallel workers, each with its own copy of the networks and environment. Workers compute gradients independently and asynchronously update a global network. This avoids the need for replay buffers and was thought to decorrelate data.\n",
    "- **A2C:** Waits for all workers (or a single worker collecting a batch) to finish a segment of experience. Gradients are calculated based on the full batch and applied synchronously. Empirically, A2C often performs as well or better than A3C, is simpler to implement (especially on GPUs), and utilizes hardware more effectively.\n",
    "\n",
    "### Why Use Advantages?\n",
    "Using the advantage function $A_t$ instead of the raw return $G_t$ (like REINFORCE) or TD error $\\delta_t$ significantly reduces the variance of the policy gradient estimate. Subtracting the state value $V(s_t)$ acts as a baseline, centering the updates around zero. Actions better than average get positive reinforcement, actions worse than average get negative reinforcement, leading to more stable and efficient learning.\n",
    "\n",
    "## Where and How A2C is Used\n",
    "\n",
    "A2C is a strong baseline algorithm used in various RL domains:\n",
    "\n",
    "1.  **Classic Control & Benchmarks:** Effective on tasks like CartPole, Pendulum, Acrobot.\n",
    "2.  **Atari Games:** Can achieve good performance, though often surpassed by more advanced methods like PPO or Rainbow.\n",
    "3.  **Continuous Control:** Applicable with appropriate policy distributions (e.g., Gaussian).\n",
    "4.  **Foundation:** Serves as a simpler synchronous counterpart to A3C and shares many concepts with PPO.\n",
    "\n",
    "A2C is suitable when:\n",
    "- A balance between the simplicity of REINFORCE and the complexity/performance of PPO/TRPO is desired.\n",
    "- On-policy interaction is feasible.\n",
    "- Lower variance updates than REINFORCE are needed.\n",
    "- Synchronous updates are preferred over asynchronous ones (easier debugging, better GPU utilization).\n",
    "\n",
    "## Mathematical Foundation of A2C\n",
    "\n",
    "A2C aims to optimize both the actor (policy $\\pi_\theta$) and the critic (value function $V_\\phi$).\n",
    "\n",
    "### Actor Update (Policy Gradient with Advantage)\n",
    "The actor parameters $\\theta$ are updated using gradient ascent on the policy objective $J(\\theta)$. The gradient is estimated using the advantage provided by the critic:\n",
    "$$ \\nabla_\\theta J(\\theta) \\approx \\mathbb{E}_t [ \\nabla_\\theta \\log \\pi_\\theta(a_t | s_t) \\hat{A}_t ] $$\n",
    "where $\\hat{A}_t$ is the estimated advantage for the state-action pair $(s_t, a_t)$ taken at time $t$.\n",
    "The update rule encourages actions that lead to positive advantages and discourages those leading to negative advantages.\n",
    "\n",
    "### Critic Update (Value Function Estimation)\n",
    "The critic parameters $\\phi$ are updated to minimize the error between the predicted state value $V_\\phi(s_t)$ and a target value $R_t$. The target $R_t$ is typically an estimate of the true value $V^{\\pi}(s_t)$, often computed using n-step returns or based on GAE.\n",
    "The loss function is usually the mean squared error (MSE):\n",
    "$$ L^{VF}(\\phi) = \\mathbb{E}_t [ (R_t - V_\\phi(s_t))^2 ] $$\n",
    "Minimizing this loss makes the critic a better predictor of future returns.\n",
    "\n",
    "### Advantage Estimation (n-step or GAE)\n",
    "The advantage $\\hat{A}_t$ can be estimated in several ways:\n",
    "1.  **One-step (TD error):** $\\hat{A}_t = r_{t+1} + \\gamma V_\\phi(s_{t+1}) - V_\\phi(s_t) = \\delta_t$. Simple but can be biased.\n",
    "2.  **n-step:** $\\hat{A}_t = (\\sum_{k=0}^{n-1} \\gamma^k r_{t+k+1}) + \\gamma^n V_\\phi(s_{t+n}) - V_\\phi(s_t)$. Balances bias and variance based on $n$.\n",
    "3.  **GAE (Generalized Advantage Estimation):** Commonly used with A2C/PPO for a good bias-variance trade-off, identical to its use in TRPO/PPO.\n",
    "    $$ \\hat{A}^{GAE}_t = \\sum_{l=0}^{\\infty} (\\gamma \\lambda)^l \\delta_{t+l}, \\quad \\text{where} \\quad \\delta_t = r_t + \\gamma V_\\phi(s_{t+1}) - V_\\phi(s_t) $$\n",
    "    The target for the critic update when using GAE is often $R_t = \\hat{A}^{GAE}_t + V_\\phi(s_t)$.\n",
    "\n",
    "### Entropy Bonus\n",
    "Similar to PPO, an entropy bonus is often added to the actor's objective to encourage exploration:\n",
    "$$ J_{entropy}(\\theta) = \\mathbb{E}_t [ H(\\pi_\theta(\\cdot|s_t)) ] = \\mathbb{E}_t [ \\mathbb{E}_{a \\sim \\pi_\theta(\\cdot|s_t)} [-\\log \\pi_\theta(a|s_t)] ] $$\n",
    "\n",
    "### Combined Loss\n",
    "The updates for actor and critic are often performed simultaneously using a combined loss function. Assuming gradient ascent for the actor objective and gradient descent for the critic loss, the overall loss to *minimize* is:\n",
    "$$ L^{A2C}(\\theta, \\phi) = \\mathbb{E}_t [ -\\log \\pi_\\theta(a_t | s_t) \\hat{A}_t^{\\text{detached}} - c_e H(\\pi_\theta(\\cdot|s_t)) + c_v (R_t - V_\\phi(s_t))^2 ] $$\n",
    "Where $c_e$ and $c_v$ are coefficients for the entropy bonus and value loss, respectively. Note that $\\hat{A}_t$ is treated as a constant (detached) when computing the policy gradient to avoid interfering gradients.\n",
    "\n",
    "## Step-by-Step Explanation of A2C\n",
    "\n",
    "1.  **Initialize**: Actor network $\\pi(a|s; \\theta)$, Critic network $V(s; \\phi)$, hyperparameters ($\\gamma, \\lambda$, learning rates $\\alpha_\theta, \\alpha_\\phi$, entropy coeff $c_e$, value coeff $c_v$, rollout length $N$).\n",
    "2.  **For each iteration**:\n",
    "    a.  **Collect Trajectories**: Run the current policy $\\pi_\theta$ for $N$ steps (or across multiple parallel environments for $N$ total steps), collecting $(s_t, a_t, r_{t+1}, s_{t+1}, d_t, \\log \\pi_\theta(a_t|s_t))$ for $t=0...N-1$. If the last state $s_N$ is not terminal, bootstrap its value $V_\\phi(s_N)$.\n",
    "    b.  **Estimate Advantages & Returns**: Compute $\\hat{A}_t$ (e.g., using GAE) and target returns $R_t = \\hat{A}_t + V_\\phi(s_t)$ for $t=0...N-1$ using the collected rewards and value estimates $V_\\phi(s_t)$.\n",
    "    c.  **Compute Combined Loss**: Calculate the A2C loss $L^{A2C}$ over the collected batch using current network outputs (log probs, entropy, predicted values) and the calculated targets (advantages, returns-to-go).\n",
    "    d.  **Update Networks**: Perform a single gradient descent step on $L^{A2C}$ to update both $\\theta$ and $\\phi$ (using separate optimizers or a shared one if applicable).\n",
    "3.  **Repeat**: Until convergence.\n",
    "\n",
    "## Key Components of A2C\n",
    "\n",
    "### Policy Network (Actor)\n",
    "- Learns the policy $\\pi(a|s; \\theta)$. Updated based on advantage estimates.\n",
    "\n",
    "### Value Network (Critic)\n",
    "- Learns the state-value function $V(s; \\phi)$. Provides baseline for advantage calculation and target values.\n",
    "- Updated based on TD errors or returns-to-go.\n",
    "\n",
    "### Rollout Collection (On-Policy, Batched)\n",
    "- Collects a batch of experience (typically fixed number of steps $N$) using the current policy.\n",
    "\n",
    "### Advantage Estimation\n",
    "- Computes $\\hat{A}_t = R_t - V(s_t)$, often using GAE for $R_t$ (implicitly via $\\hat{A}^{GAE}_t$). Provides a variance-reduced signal for policy updates.\n",
    "\n",
    "### Combined Actor-Critic Loss\n",
    "- Integrates policy gradient loss (weighted by advantages), value function loss (MSE), and entropy bonus into a single objective for optimization.\n",
    "\n",
    "### Synchronous Updates\n",
    "- All gradients from the batch are computed and applied together, simplifying implementation and debugging compared to A3C.\n",
    "\n",
    "### Hyperparameters\n",
    "- Learning rates for actor and critic.\n",
    "- Discount factor $\\gamma$.\n",
    "- GAE $\\lambda$ (if used).\n",
    "- Rollout length $N$.\n",
    "- Entropy coefficient $c_e$.\n",
    "- Value loss coefficient $c_v$.\n",
    "\n",
    "## Practical Example: Custom Grid World\n",
    "\n",
    "We continue with the custom Grid World environment to maintain consistency and demonstrate A2C's mechanics in a simple setting.\n",
    "\n",
    "**Environment Description:** (Same as before)\n",
    "- Grid Size: 10x10.\n",
    "- State: `[row/9, col/9]`\n",
    "- Actions: 4 discrete (Up, Down, Left, Right).\n",
    "- Start: (0, 0), Goal: (9, 9).\n",
    "- Rewards: +10 (goal), -1 (wall), -0.1 (step).\n",
    "- Termination: Goal or max steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting up the Environment\n",
    "\n",
    "Import libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cpu\n"
     ]
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import math\n",
    "from collections import namedtuple, deque \n",
    "from itertools import count\n",
    "from typing import List, Tuple, Dict, Optional, Callable\n",
    "\n",
    "# Import PyTorch\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "from torch.distributions import Categorical\n",
    "\n",
    "# Set up device\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"Using device: {device}\")\n",
    "\n",
    "# Set random seeds for reproducibility\n",
    "seed = 42\n",
    "random.seed(seed)\n",
    "np.random.seed(seed)\n",
    "torch.manual_seed(seed)\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.manual_seed_all(seed)\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating the Custom Environment\n",
    "\n",
    "Using the same `GridEnvironment` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Custom Grid World Environment (Identical to previous notebooks)\n",
    "class GridEnvironment:\n",
    "    \"\"\"\n",
    "    A simple 10x10 Grid World environment.\n",
    "    State: (row, col) represented as normalized vector [row/9, col/9].\n",
    "    Actions: 0 (up), 1 (down), 2 (left), 3 (right).\n",
    "    Rewards: +10 for reaching the goal, -1 for hitting a wall, -0.1 for each step.\n",
    "    \"\"\"\n",
    "    def __init__(self, rows: int = 10, cols: int = 10) -> None:\n",
    "        self.rows: int = rows\n",
    "        self.cols: int = cols\n",
    "        self.start_state: Tuple[int, int] = (0, 0)\n",
    "        self.goal_state: Tuple[int, int] = (rows - 1, cols - 1)\n",
    "        self.state: Tuple[int, int] = self.start_state\n",
    "        self.state_dim: int = 2\n",
    "        self.action_dim: int = 4\n",
    "        self.action_map: Dict[int, Tuple[int, int]] = {0: (-1, 0), 1: (1, 0), 2: (0, -1), 3: (0, 1)}\n",
    "\n",
    "    def reset(self) -> torch.Tensor:\n",
    "        self.state = self.start_state\n",
    "        return self._get_state_tensor(self.state)\n",
    "\n",
    "    def _get_state_tensor(self, state_tuple: Tuple[int, int]) -> torch.Tensor:\n",
    "        norm_row = state_tuple[0] / (self.rows - 1) if self.rows > 1 else 0.0\n",
    "        norm_col = state_tuple[1] / (self.cols - 1) if self.cols > 1 else 0.0\n",
    "        normalized_state: List[float] = [norm_row, norm_col]\n",
    "        return torch.tensor(normalized_state, dtype=torch.float32, device=device)\n",
    "\n",
    "    def step(self, action: int) -> Tuple[torch.Tensor, float, bool]:\n",
    "        if self.state == self.goal_state:\n",
    "            return self._get_state_tensor(self.state), 0.0, True\n",
    "        dr, dc = self.action_map[action]\n",
    "        current_row, current_col = self.state\n",
    "        next_row, next_col = current_row + dr, current_col + dc\n",
    "        reward: float = -0.1\n",
    "        if not (0 <= next_row < self.rows and 0 <= next_col < self.cols):\n",
    "            next_row, next_col = current_row, current_col\n",
    "            reward = -1.0\n",
    "        self.state = (next_row, next_col)\n",
    "        next_state_tensor: torch.Tensor = self._get_state_tensor(self.state)\n",
    "        done: bool = (self.state == self.goal_state)\n",
    "        if done:\n",
    "            reward = 10.0\n",
    "        return next_state_tensor, reward, done\n",
    "\n",
    "    def get_action_space_size(self) -> int:\n",
    "        return self.action_dim\n",
    "\n",
    "    def get_state_dimension(self) -> int:\n",
    "        return self.state_dim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instantiate and test the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Custom Grid Environment:\n",
      "Size: 10x10\n",
      "State Dim: 2\n",
      "Action Dim: 4\n",
      "Example state tensor for (0,0): tensor([0., 0.])\n"
     ]
    }
   ],
   "source": [
    "custom_env = GridEnvironment(rows=10, cols=10)\n",
    "n_actions_custom = custom_env.get_action_space_size()\n",
    "n_observations_custom = custom_env.get_state_dimension()\n",
    "\n",
    "print(f\"Custom Grid Environment:\")\n",
    "print(f\"Size: {custom_env.rows}x{custom_env.cols}\")\n",
    "print(f\"State Dim: {n_observations_custom}\")\n",
    "print(f\"Action Dim: {n_actions_custom}\")\n",
    "start_state_tensor = custom_env.reset()\n",
    "print(f\"Example state tensor for (0,0): {start_state_tensor}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementing the A2C Algorithm\n",
    "\n",
    "Define the Actor and Critic networks, GAE calculation, and the combined A2C update function.\n",
    "\n",
    "### Defining the Actor Network\n",
    "\n",
    "Outputs action probabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the Policy Network (Actor)\n",
    "class PolicyNetwork(nn.Module):\n",
    "    \"\"\" MLP Actor network for A2C \"\"\"\n",
    "    def __init__(self, n_observations: int, n_actions: int):\n",
    "        super(PolicyNetwork, self).__init__()\n",
    "        self.layer1 = nn.Linear(n_observations, 128)\n",
    "        self.layer2 = nn.Linear(128, 128)\n",
    "        self.layer3 = nn.Linear(128, n_actions)\n",
    "\n",
    "    def forward(self, x: torch.Tensor) -> Categorical:\n",
    "        \"\"\" Forward pass, returns a Categorical distribution. \"\"\"\n",
    "        if not isinstance(x, torch.Tensor):\n",
    "             x = torch.tensor(x, dtype=torch.float32, device=device)\n",
    "        elif x.dtype != torch.float32:\n",
    "             x = x.to(dtype=torch.float32)\n",
    "        if x.dim() == 1:\n",
    "            x = x.unsqueeze(0)\n",
    "        x = F.relu(self.layer1(x))\n",
    "        x = F.relu(self.layer2(x))\n",
    "        action_logits = self.layer3(x)\n",
    "        return Categorical(logits=action_logits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the Critic Network\n",
    "\n",
    "Outputs a single state value estimate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the Value Network (Critic)\n",
    "class ValueNetwork(nn.Module):\n",
    "    \"\"\" MLP Critic network for A2C \"\"\"\n",
    "    def __init__(self, n_observations: int):\n",
    "        super(ValueNetwork, self).__init__()\n",
    "        self.layer1 = nn.Linear(n_observations, 128)\n",
    "        self.layer2 = nn.Linear(128, 128)\n",
    "        self.layer3 = nn.Linear(128, 1)\n",
    "\n",
    "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
    "        \"\"\" Forward pass, returns the estimated state value. \"\"\"\n",
    "        if not isinstance(x, torch.Tensor):\n",
    "             x = torch.tensor(x, dtype=torch.float32, device=device)\n",
    "        elif x.dtype != torch.float32:\n",
    "             x = x.to(dtype=torch.float32)\n",
    "        if x.dim() == 1:\n",
    "            x = x.unsqueeze(0)\n",
    "        x = F.relu(self.layer1(x))\n",
    "        x = F.relu(self.layer2(x))\n",
    "        state_value = self.layer3(x)\n",
    "        return state_value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating Returns and Advantages (using GAE)\n",
    "\n",
    "Reusing the GAE function. Note that we also need the returns-to-go ($R_t$) as the target for the critic update."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_gae_and_returns(rewards: torch.Tensor, \n",
    "                            values: torch.Tensor, \n",
    "                            next_values: torch.Tensor, \n",
    "                            dones: torch.Tensor, \n",
    "                            gamma: float, \n",
    "                            lambda_gae: float, \n",
    "                            standardize_adv: bool = True) -> Tuple[torch.Tensor, torch.Tensor]:\n",
    "    \"\"\"\n",
    "    Computes Generalized Advantage Estimation (GAE) and the returns-to-go (value targets).\n",
    "\n",
    "    Parameters:\n",
    "    - rewards, values, next_values, dones: Tensors collected from rollout.\n",
    "    - gamma (float): Discount factor.\n",
    "    - lambda_gae (float): GAE smoothing parameter.\n",
    "    - standardize_adv (bool): Whether to standardize advantages.\n",
    "\n",
    "    Returns:\n",
    "    - Tuple[torch.Tensor, torch.Tensor]: \n",
    "        - advantages: Tensor of GAE advantages.\n",
    "        - returns_to_go: Tensor of target values for the critic (Advantage + V_old).\n",
    "    \"\"\"\n",
    "    advantages = torch.zeros_like(rewards)\n",
    "    last_advantage = 0.0\n",
    "    n_steps = len(rewards)\n",
    "\n",
    "    # Calculate advantages using GAE formula backwards\n",
    "    for t in reversed(range(n_steps)):\n",
    "        mask = 1.0 - dones[t]\n",
    "        delta = rewards[t] + gamma * next_values[t] * mask - values[t]\n",
    "        advantages[t] = delta + gamma * lambda_gae * last_advantage * mask\n",
    "        last_advantage = advantages[t]\n",
    "\n",
    "    # Calculate returns-to-go (targets for value function)\n",
    "    returns_to_go = advantages + values # R_t = A_t + V(s_t)\n",
    "\n",
    "    # Standardize advantages (optional but usually recommended)\n",
    "    if standardize_adv:\n",
    "        mean_adv = torch.mean(advantages)\n",
    "        std_adv = torch.std(advantages) + 1e-8\n",
    "        advantages = (advantages - mean_adv) / std_adv\n",
    "        \n",
    "    return advantages, returns_to_go"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The A2C Update Step\n",
    "\n",
    "This function computes the combined loss for actor and critic and performs a single optimization step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_a2c(actor: PolicyNetwork,\n",
    "               critic: ValueNetwork,\n",
    "               actor_optimizer: optim.Optimizer,\n",
    "               critic_optimizer: optim.Optimizer, # Separate optimizers common\n",
    "               states: torch.Tensor,\n",
    "               actions: torch.Tensor,\n",
    "               advantages: torch.Tensor,\n",
    "               returns_to_go: torch.Tensor,\n",
    "               value_loss_coeff: float,\n",
    "               entropy_coeff: float) -> Tuple[float, float, float]:\n",
    "    \"\"\"\n",
    "    Performs one synchronous update step for both Actor and Critic.\n",
    "\n",
    "    Parameters:\n",
    "    - actor, critic: The networks.\n",
    "    - actor_optimizer, critic_optimizer: The optimizers.\n",
    "    - states, actions, advantages, returns_to_go: Batch data tensors.\n",
    "    - value_loss_coeff (float): Coefficient for the value loss.\n",
    "    - entropy_coeff (float): Coefficient for the entropy bonus.\n",
    "\n",
    "    Returns:\n",
    "    - Tuple[float, float, float]: Policy loss, value loss, and entropy for this update step.\n",
    "    \"\"\"\n",
    "    # --- Evaluate current networks --- \n",
    "    policy_dist = actor(states)\n",
    "    log_probs = policy_dist.log_prob(actions)\n",
    "    entropy = policy_dist.entropy().mean()\n",
    "    values_pred = critic(states).squeeze()\n",
    "\n",
    "    # --- Calculate Losses --- \n",
    "    # Policy Loss (Actor): - E[log_pi * A] - entropy_bonus\n",
    "    # Advantages are detached as they shouldn't propagate gradients to the critic here\n",
    "    policy_loss = -(log_probs * advantages.detach()).mean() - entropy_coeff * entropy\n",
    "    \n",
    "    # Value Loss (Critic): MSE(V_pred, R_target)\n",
    "    # Returns-to-go are detached as they are targets\n",
    "    value_loss = F.mse_loss(values_pred, returns_to_go.detach())\n",
    "    \n",
    "    # Combined loss (optional, can also optimize separately)\n",
    "    # total_loss = policy_loss + value_loss_coeff * value_loss\n",
    "\n",
    "    # --- Optimize Actor --- \n",
    "    actor_optimizer.zero_grad()\n",
    "    policy_loss.backward() # Computes gradients only for actor parameters\n",
    "    actor_optimizer.step()\n",
    "\n",
    "    # --- Optimize Critic --- \n",
    "    critic_optimizer.zero_grad()\n",
    "    # Need to scale value loss *before* backward if using combined loss and single backward()\n",
    "    # If optimizing separately, backward() uses the unscaled loss.\n",
    "    (value_loss_coeff * value_loss).backward() # Computes gradients only for critic parameters\n",
    "    critic_optimizer.step()\n",
    "\n",
    "    # Return individual loss components for logging\n",
    "    return policy_loss.item() + entropy_coeff * entropy.item(), value_loss.item(), entropy.item()\n",
    "    # Return the policy objective part (-log_pi*A), the value loss, and the entropy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running the A2C Algorithm\n",
    "\n",
    "Set up hyperparameters, initialize networks/optimizers, and run the A2C training loop.\n",
    "\n",
    "### Hyperparameter Setup\n",
    "\n",
    "Define A2C hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameters for A2C on Custom Grid World\n",
    "GAMMA_A2C = 0.99             # Discount factor\n",
    "GAE_LAMBDA_A2C = 0.95        # GAE lambda parameter\n",
    "ACTOR_LR_A2C = 3e-4          # Learning rate for the actor\n",
    "CRITIC_LR_A2C = 1e-3         # Learning rate for the critic\n",
    "VALUE_LOSS_COEFF_A2C = 0.5   # Coefficient for value loss\n",
    "ENTROPY_COEFF_A2C = 0.01     # Coefficient for entropy bonus\n",
    "STANDARDIZE_ADV_A2C = True   # Whether to standardize advantages\n",
    "\n",
    "NUM_ITERATIONS_A2C = 400    # Number of A2C updates (iterations)\n",
    "STEPS_PER_ITERATION_A2C = 256 # Number of steps (batch size) collected per iteration\n",
    "MAX_STEPS_PER_EPISODE_A2C = 200 # Max steps per episode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialization\n",
    "\n",
    "Initialize actor, critic, and their optimizers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-instantiate the environment\n",
    "custom_env: GridEnvironment = GridEnvironment(rows=10, cols=10)\n",
    "n_actions_custom: int = custom_env.get_action_space_size()\n",
    "n_observations_custom: int = custom_env.get_state_dimension()\n",
    "\n",
    "# Initialize Actor and Critic\n",
    "actor_a2c: PolicyNetwork = PolicyNetwork(n_observations_custom, n_actions_custom).to(device)\n",
    "critic_a2c: ValueNetwork = ValueNetwork(n_observations_custom).to(device)\n",
    "\n",
    "# Initialize Optimizers\n",
    "actor_optimizer_a2c: optim.Adam = optim.Adam(actor_a2c.parameters(), lr=ACTOR_LR_A2C)\n",
    "critic_optimizer_a2c: optim.Adam = optim.Adam(critic_a2c.parameters(), lr=CRITIC_LR_A2C)\n",
    "\n",
    "# Lists for plotting\n",
    "a2c_iteration_rewards = []\n",
    "a2c_iteration_avg_ep_lens = []\n",
    "a2c_iteration_policy_losses = []\n",
    "a2c_iteration_value_losses = []\n",
    "a2c_iteration_entropies = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Loop\n",
    "\n",
    "The A2C training loop: collect a batch of N steps, compute advantages/returns, perform one update for actor and critic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting A2C Training on Custom Grid World...\n",
      "Iter 50/400 | Avg Reward: -1.55 | Avg Len: 60.2 | P_Loss: -0.0259 | V_Loss: 14.3651 | Entropy: 1.3115\n",
      "Iter 100/400 | Avg Reward: 6.08 | Avg Len: 22.2 | P_Loss: -0.0787 | V_Loss: 20.6416 | Entropy: 0.9647\n",
      "Iter 150/400 | Avg Reward: 6.54 | Avg Len: 19.7 | P_Loss: -0.0793 | V_Loss: 9.2166 | Entropy: 0.8163\n",
      "Iter 200/400 | Avg Reward: 6.85 | Avg Len: 19.3 | P_Loss: -0.0320 | V_Loss: 1.6230 | Entropy: 0.7364\n",
      "Iter 250/400 | Avg Reward: 7.31 | Avg Len: 18.2 | P_Loss: -0.0797 | V_Loss: 1.5631 | Entropy: 0.6973\n",
      "Iter 300/400 | Avg Reward: 7.53 | Avg Len: 18.0 | P_Loss: -0.0521 | V_Loss: 0.7190 | Entropy: 0.6447\n",
      "Iter 350/400 | Avg Reward: 7.89 | Avg Len: 18.3 | P_Loss: -0.0556 | V_Loss: 0.4610 | Entropy: 0.6319\n",
      "Iter 400/400 | Avg Reward: 7.89 | Avg Len: 18.2 | P_Loss: -0.0499 | V_Loss: 0.2122 | Entropy: 0.5835\n",
      "Custom Grid World Training Finished (A2C).\n"
     ]
    }
   ],
   "source": [
    "print(\"Starting A2C Training on Custom Grid World...\")\n",
    "\n",
    "# --- A2C Training Loop ---\n",
    "current_state = custom_env.reset() # Start with initial state\n",
    "\n",
    "for iteration in range(NUM_ITERATIONS_A2C):\n",
    "    # --- 1. Collect Trajectories (N steps) --- \n",
    "    batch_states_list: List[torch.Tensor] = []\n",
    "    batch_actions_list: List[int] = []\n",
    "    batch_log_probs_list: List[torch.Tensor] = []\n",
    "    batch_rewards_list: List[float] = []\n",
    "    batch_values_list: List[torch.Tensor] = [] # Store V(s_t)\n",
    "    batch_dones_list: List[float] = []\n",
    "    \n",
    "    episode_rewards_in_iter: List[float] = []\n",
    "    episode_lengths_in_iter: List[int] = []\n",
    "    current_episode_reward = 0.0\n",
    "    current_episode_length = 0\n",
    "\n",
    "    for step in range(STEPS_PER_ITERATION_A2C):\n",
    "        state_tensor = current_state\n",
    "        \n",
    "        # Sample action and get value estimate\n",
    "        with torch.no_grad():\n",
    "            policy_dist = actor_a2c(state_tensor)\n",
    "            value_estimate = critic_a2c(state_tensor)\n",
    "            action_tensor = policy_dist.sample()\n",
    "            action = action_tensor.item()\n",
    "            log_prob = policy_dist.log_prob(action_tensor)\n",
    "            \n",
    "        # Store data\n",
    "        batch_states_list.append(state_tensor)\n",
    "        batch_actions_list.append(action)\n",
    "        batch_log_probs_list.append(log_prob)\n",
    "        batch_values_list.append(value_estimate)\n",
    "\n",
    "        # Interact\n",
    "        next_state, reward, done = custom_env.step(action)\n",
    "        \n",
    "        batch_rewards_list.append(reward)\n",
    "        batch_dones_list.append(float(done))\n",
    "        \n",
    "        current_state = next_state\n",
    "        current_episode_reward += reward\n",
    "        current_episode_length += 1\n",
    "        \n",
    "        # Handle episode termination within the batch collection\n",
    "        if done or current_episode_length >= MAX_STEPS_PER_EPISODE_A2C:\n",
    "            episode_rewards_in_iter.append(current_episode_reward)\n",
    "            episode_lengths_in_iter.append(current_episode_length)\n",
    "            current_state = custom_env.reset() # Reset for the next episode\n",
    "            current_episode_reward = 0.0\n",
    "            current_episode_length = 0\n",
    "\n",
    "    # --- End Rollout --- \n",
    "\n",
    "    # Bootstrap value for the last state if the episode didn't end\n",
    "    with torch.no_grad():\n",
    "        last_value = critic_a2c(current_state).squeeze() # Value of the state we'd start the next step from\n",
    "        \n",
    "    # Convert lists to tensors\n",
    "    states_tensor = torch.stack(batch_states_list).to(device).squeeze(1) # Remove extra dim if added by network\n",
    "    actions_tensor = torch.tensor(batch_actions_list, dtype=torch.long, device=device)\n",
    "    log_probs_tensor = torch.stack(batch_log_probs_list).squeeze().to(device)\n",
    "    rewards_tensor = torch.tensor(batch_rewards_list, dtype=torch.float32, device=device)\n",
    "    values_tensor = torch.cat(batch_values_list).squeeze().to(device)\n",
    "    dones_tensor = torch.tensor(batch_dones_list, dtype=torch.float32, device=device)\n",
    "\n",
    "    # Need next_values for GAE calculation\n",
    "    # Shift values and append the bootstrapped last_value\n",
    "    next_values_tensor = torch.cat((values_tensor[1:], last_value.unsqueeze(0)))\n",
    "\n",
    "    # --- 2. Estimate Advantages & Returns-to-go --- \n",
    "    advantages_tensor, returns_to_go_tensor = compute_gae_and_returns(\n",
    "        rewards_tensor, values_tensor, next_values_tensor, dones_tensor, \n",
    "        GAMMA_A2C, GAE_LAMBDA_A2C, standardize_adv=STANDARDIZE_ADV_A2C\n",
    "    )\n",
    "\n",
    "    # --- 3. Perform A2C Update --- \n",
    "    avg_policy_loss, avg_value_loss, avg_entropy = update_a2c(\n",
    "        actor_a2c, critic_a2c, actor_optimizer_a2c, critic_optimizer_a2c,\n",
    "        states_tensor, actions_tensor, advantages_tensor, returns_to_go_tensor,\n",
    "        VALUE_LOSS_COEFF_A2C, ENTROPY_COEFF_A2C\n",
    "    )\n",
    "\n",
    "    # --- Logging --- \n",
    "    avg_reward_iter = np.mean(episode_rewards_in_iter) if episode_rewards_in_iter else np.nan\n",
    "    avg_len_iter = np.mean(episode_lengths_in_iter) if episode_lengths_in_iter else np.nan\n",
    "\n",
    "    a2c_iteration_rewards.append(avg_reward_iter)\n",
    "    a2c_iteration_avg_ep_lens.append(avg_len_iter)\n",
    "    a2c_iteration_policy_losses.append(avg_policy_loss)\n",
    "    a2c_iteration_value_losses.append(avg_value_loss)\n",
    "    a2c_iteration_entropies.append(avg_entropy)\n",
    "\n",
    "    if (iteration + 1) % 50 == 0: # Print less frequently for potentially longer training\n",
    "        print(f\"Iter {iteration+1}/{NUM_ITERATIONS_A2C} | Avg Reward: {avg_reward_iter:.2f} | Avg Len: {avg_len_iter:.1f} | P_Loss: {avg_policy_loss:.4f} | V_Loss: {avg_value_loss:.4f} | Entropy: {avg_entropy:.4f}\")\n",
    "\n",
    "print(\"Custom Grid World Training Finished (A2C).\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the Learning Process\n",
    "\n",
    "Plot the results for the A2C agent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting results for A2C on Custom Grid World\n",
    "plt.figure(figsize=(20, 8))\n",
    "\n",
    "# Average Rewards per Iteration\n",
    "plt.subplot(2, 3, 1)\n",
    "valid_rewards_a2c = [r for r in a2c_iteration_rewards if not np.isnan(r)]\n",
    "valid_indices_a2c = [i for i, r in enumerate(a2c_iteration_rewards) if not np.isnan(r)]\n",
    "plt.plot(valid_indices_a2c, valid_rewards_a2c)\n",
    "plt.title('A2C Custom Grid: Avg Ep Reward / Iteration')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Avg Reward')\n",
    "plt.grid(True)\n",
    "if len(valid_rewards_a2c) >= 20: # Use larger window for potentially noisy rewards\n",
    "    rewards_ma_a2c = np.convolve(valid_rewards_a2c, np.ones(20)/20, mode='valid')\n",
    "    plt.plot(valid_indices_a2c[19:], rewards_ma_a2c, label='20-iter MA', color='orange')\n",
    "    plt.legend()\n",
    "\n",
    "# Average Episode Length per Iteration\n",
    "plt.subplot(2, 3, 2)\n",
    "valid_lens_a2c = [l for l in a2c_iteration_avg_ep_lens if not np.isnan(l)]\n",
    "valid_indices_len_a2c = [i for i, l in enumerate(a2c_iteration_avg_ep_lens) if not np.isnan(l)]\n",
    "plt.plot(valid_indices_len_a2c, valid_lens_a2c)\n",
    "plt.title('A2C Custom Grid: Avg Ep Length / Iteration')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Avg Steps')\n",
    "plt.grid(True)\n",
    "if len(valid_lens_a2c) >= 20:\n",
    "    lens_ma_a2c = np.convolve(valid_lens_a2c, np.ones(20)/20, mode='valid')\n",
    "    plt.plot(valid_indices_len_a2c[19:], lens_ma_a2c, label='20-iter MA', color='orange')\n",
    "    plt.legend()\n",
    "\n",
    "# Critic (Value) Loss per Iteration\n",
    "plt.subplot(2, 3, 3)\n",
    "plt.plot(a2c_iteration_value_losses)\n",
    "plt.title('A2C Custom Grid: Value Loss / Iteration')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('MSE Loss')\n",
    "plt.grid(True)\n",
    "if len(a2c_iteration_value_losses) >= 20:\n",
    "    vloss_ma_a2c = np.convolve(a2c_iteration_value_losses, np.ones(20)/20, mode='valid')\n",
    "    plt.plot(np.arange(len(vloss_ma_a2c)) + 19, vloss_ma_a2c, label='20-iter MA', color='orange')\n",
    "    plt.legend()\n",
    "\n",
    "# Actor (Policy) Loss per Iteration (Policy Objective Part)\n",
    "plt.subplot(2, 3, 4)\n",
    "plt.plot(a2c_iteration_policy_losses)\n",
    "plt.title('A2C Custom Grid: Policy Loss / Iteration')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Avg (-log_pi*A - Ent)')\n",
    "plt.grid(True)\n",
    "if len(a2c_iteration_policy_losses) >= 20:\n",
    "    ploss_ma_a2c = np.convolve(a2c_iteration_policy_losses, np.ones(20)/20, mode='valid')\n",
    "    plt.plot(np.arange(len(ploss_ma_a2c)) + 19, ploss_ma_a2c, label='20-iter MA', color='orange')\n",
    "    plt.legend()\n",
    "\n",
    "# Entropy per Iteration\n",
    "plt.subplot(2, 3, 5)\n",
    "plt.plot(a2c_iteration_entropies)\n",
    "plt.title('A2C Custom Grid: Policy Entropy / Iteration')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Entropy')\n",
    "plt.grid(True)\n",
    "if len(a2c_iteration_entropies) >= 20:\n",
    "    entropy_ma_a2c = np.convolve(a2c_iteration_entropies, np.ones(20)/20, mode='valid')\n",
    "    plt.plot(np.arange(len(entropy_ma_a2c)) + 19, entropy_ma_a2c, label='20-iter MA', color='orange')\n",
    "    plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis of A2C Learning Curves (Custom Grid World):**\n",
    "\n",
    "1.  **Avg Ep Reward / Iteration:**\n",
    "    The agent learns successfully, with rewards increasing steeply between iterations ~20-100 and then stably plateauing near the optimal value (~8). The curve shows significantly less variance than REINFORCE after convergence, demonstrating the stabilizing effect of the critic's baseline, though perhaps slightly slower initial convergence than PPO/REINFORCE in this run.\n",
    "\n",
    "2.  **Avg Ep Length / Iteration:**\n",
    "    Matching the reward trend, episode lengths drop rapidly during the main learning phase (~20-100 iterations) and stabilize cleanly near the optimal path length (~18 steps). This confirms the agent learned an efficient policy alongside maximizing rewards, achieving consistent, quick goal attainment.\n",
    "\n",
    "3.  **Avg Value Loss / Iteration:**\n",
    "    The critic's MSE loss initially increases as the policy rapidly changes, then decreases significantly and converges to a very low value. This indicates the value function successfully learned to accurately predict state values for the improving policy, providing a good baseline for advantage calculations and stabilizing the learning process compared to REINFORCE.\n",
    "\n",
    "4.  **Avg Policy Loss / Iteration:**\n",
    "    The policy loss (actor's objective, including entropy) decreases substantially (becomes more negative) during learning, signifying effective policy improvement driven by advantage estimates. It exhibits moderate variance, less than REINFORCE but potentially more than PPO's clipped objective. The gradual rise towards zero later might reflect smaller advantages and decreasing entropy near convergence.\n",
    "\n",
    "5.  **Avg Policy Entropy / Iteration:**\n",
    "    Entropy starts high, facilitating exploration, and decreases smoothly and steadily throughout training as the policy becomes more deterministic. This controlled reduction shows the agent balancing exploration with exploitation, converging towards a more confident, optimal policy without premature collapse.\n",
    "\n",
    "**Overall Conclusion:**\n",
    "A2C effectively solves the Grid World, demonstrating stable learning characteristic of actor-critic methods due to variance reduction via the value baseline. It converges efficiently to a near-optimal policy, balancing reward maximization and path efficiency. The learning curves for loss and entropy show expected behavior, confirming the algorithm's components are working correctly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing the Learned Policy (Optional Visualization)\n",
    "\n",
    "Visualize the policy learned by the A2C actor network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Plotting Learned Policy from A2C:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reusing the policy plotting function (works for Actor networks outputting Categorical)\n",
    "def plot_a2c_policy_grid(policy_net: PolicyNetwork, env: GridEnvironment, device: torch.device) -> None:\n",
    "    \"\"\"\n",
    "    Plots the greedy policy derived from the A2C policy network.\n",
    "    Shows the most likely action for each state.\n",
    "    (Identical to the REINFORCE/TRPO/PPO plotting function)\n",
    "    \"\"\"\n",
    "    rows: int = env.rows\n",
    "    cols: int = env.cols\n",
    "    policy_grid: np.ndarray = np.empty((rows, cols), dtype=str)\n",
    "    action_symbols: Dict[int, str] = {0: '↑', 1: '↓', 2: '←', 3: '→'}\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(cols * 0.6, rows * 0.6))\n",
    "\n",
    "    for r in range(rows):\n",
    "        for c in range(cols):\n",
    "            state_tuple: Tuple[int, int] = (r, c)\n",
    "            if state_tuple == env.goal_state:\n",
    "                policy_grid[r, c] = 'G'\n",
    "                ax.text(c, r, 'G', ha='center', va='center', color='green', fontsize=12, weight='bold')\n",
    "            else:\n",
    "                state_tensor: torch.Tensor = env._get_state_tensor(state_tuple)\n",
    "                with torch.no_grad():\n",
    "                    action_dist: Categorical = policy_net(state_tensor)\n",
    "                    best_action: int = action_dist.probs.argmax(dim=1).item()\n",
    "\n",
    "                policy_grid[r, c] = action_symbols[best_action]\n",
    "                ax.text(c, r, policy_grid[r, c], ha='center', va='center', color='black', fontsize=12)\n",
    "\n",
    "    ax.matshow(np.zeros((rows, cols)), cmap='Greys', alpha=0.1)\n",
    "    ax.set_xticks(np.arange(-.5, cols, 1), minor=True)\n",
    "    ax.set_yticks(np.arange(-.5, rows, 1), minor=True)\n",
    "    ax.grid(which='minor', color='black', linestyle='-', linewidth=1)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    ax.set_title(\"A2C Learned Policy (Most Likely Action)\")\n",
    "    plt.show()\n",
    "\n",
    "# Plot the policy learned by the trained A2C actor\n",
    "print(\"\\nPlotting Learned Policy from A2C:\")\n",
    "plot_a2c_policy_grid(actor_a2c, custom_env, device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Common Challenges and Solutions in A2C\n",
    "\n",
    "**Challenge: Correlated Updates within a Batch**\n",
    "*   **Problem:** In the synchronous version (especially with a single worker), the batch of $N$ steps comes from a single trajectory segment. These consecutive samples are highly correlated, which can reduce gradient quality and learning stability compared to uncorrelated samples (like DQN's replay buffer) or diverse samples (like A3C's asynchronous updates).\n",
    "*   **Solutions**:\n",
    "    *   **Use Multiple Parallel Workers:** The standard A2C implementation often uses multiple environments running in parallel to collect the batch of $N$ steps. This introduces diversity into the batch, decorrelating the data and improving stability.\n",
    "    *   **Increase Batch Size ($N$):** Larger batches can average out some noise but increase the time between updates.\n",
    "\n",
    "**Challenge: Sensitivity to Hyperparameters**\n",
    "*   **Problem:** Performance depends on tuning learning rates, entropy/value coefficients, rollout length ($N$), and GAE parameters.\n",
    "*   **Solutions**:\n",
    "    *   **Careful Tuning:** Experiment with values, often starting with common defaults.\n",
    "    *   **Learning Rate Scheduling:** Decaying learning rates can help stabilize later stages of training.\n",
    "    *   **Entropy Scheduling:** Sometimes decaying the entropy coefficient $c_e$ is beneficial.\n",
    "\n",
    "**Challenge: Value Function Accuracy**\n",
    "*   **Problem:** As with all actor-critic methods using advantage estimates, an inaccurate critic leads to noisy or biased advantage signals, hindering actor learning.\n",
    "  **Solutions**:\n",
    "    *   **Tune Critic Learning Rate / Value Coefficient:** Ensure the critic learns effectively without overpowering the policy gradient.\n",
    "    *   **Multiple Critic Updates per Actor Update:** Sometimes performing more updates on the critic per batch helps it keep up with the changing policy (though less common in basic A2C than in some off-policy methods).\n",
    "\n",
    "**Challenge: Sample Inefficiency (On-Policy)**\n",
    "*   **Problem:** Like other on-policy methods, data is discarded after the update.\n",
    "   **Solutions**:\n",
    "    *   **PPO:** PPO explicitly allows multiple epochs over the same data, significantly improving sample efficiency over A2C.\n",
    "    *   **Off-Policy Actor-Critic:** Algorithms like DDPG, TD3, SAC use replay buffers for much greater sample efficiency.\n",
    "\n",
    "## Conclusion\n",
    "\n",
    "Advantage Actor-Critic (A2C) is a fundamental and effective actor-critic algorithm. By using a critic to estimate state values and compute advantages, it significantly reduces the gradient variance compared to REINFORCE, leading to more stable and often faster learning. Its synchronous nature makes it easier to implement and utilize hardware like GPUs compared to its asynchronous counterpart, A3C.\n",
    "\n",
    "A2C serves as a strong baseline and a conceptual bridge towards more advanced methods like PPO. While PPO often offers further improvements in stability and sample efficiency through its clipped objective and multiple update epochs, A2C remains a valuable algorithm for its relative simplicity within the actor-critic family and its clear demonstration of using value functions to improve policy gradients."
   ]
  }
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